Derek Zoolander: What is this? [smashes the model for the reading center] A center for ants?

Mugatu: What?

Derek Zoolander: How can we be expected to teach children to learn how to read… if they can’t even fit inside the building?

The humour here derives from Zoolander’s conflation of the architect’s model and the real building. He thinks the architect is offering the model as reality, but the audience is fully aware that the model is useful because it is a highly stylized and version of the real building, preserving some characteristics while unapologetically abstracting from others. Zoolander doesn’t understand the architect’s abstraction.

Criticism of economics in the public sphere often echoes Zoolander’s confusion. Critics take common modeling assumptions in theoretical economics and claim economists believe that these assumptions are literally true, akin to claiming architects don’t understand people aren’t actually ants. Promoting his new book the Guardian, John Rapley exemplifies this sort of critique:

The irony is that, in its determination to make itself a science that can reach hard and fast conclusions, economics has had to dispense with scientific method at times. For starters, it rests on a set of premises about the world not as it is, but as economists would like it to be. Just as any religious service includes a profession of faith, membership in the priesthood of economics entails certain core convictions about human nature. Among other things, most economists believe that we humans are self-interested, rational, essentially individualistic, and prefer more money to less. These articles of faith are taken as self-evident.

This argument is akin to, “most architects believe real buildings are about 20 centimeters tall.” Rapley and Zoolander share the same confusion over the purpose of models and what the model-builder believes. Economists do not believe people are always rational, etc, we rather believe that such assumptions are often useful abstractions to further the goal of understanding the incredible complexity of human societies.

Rapley and his fellow travellers in the pop-critic sphere think that economics is different from other sciences in that economists use models with false assumptions:

Deducing laws from premises deemed eternal and beyond question is a time-honoured method. For thousands of years, monks in medieval monasteries built a vast corpus of scholarship doing just that, using a method perfected by Thomas Aquinas known as scholasticism. However, this is not the method used by scientists, who tend to require assumptions to be tested empirically before a theory can be built out of them.

Real Scientists™ “require assumptions to be tested empirically before a theory can be built out of them,” whereas economists use models with assumptions which are empirically false. Economics is therefore a religion, not a science, according to Rapley. Hey, there’s even an oft-told joke along these lines,

A physicist, a chemist, and an economist who were stranded on a desert island with no implements and a can of food. The physicist and the chemist each devised an ingenious mechanism for getting the can open; the economist merely said, “Assume we have a can opener”!

The problem with arguments such as Rapley’s is that all models include false assumptions. There is no scientific model in any discipline which does not make such abstractions. A common trope of economics critics is to compare economic models to models in the Queen of the sciences, physics, and offer that economics comes up short in this comparison (“Physicists resolve their debates by looking at the data” Rapley notes, for example, confusing evidence for or against predictions with evidence for or against assumptions). But another oft-told joke is essentially the same as the one above, but the butt is physicists, not economists:

Milk production at a dairy farm was low, so the farmer wrote to the local university, asking for help from academia. A multidisciplinary team of professors was assembled, headed by a theoretical physicist, and two weeks of intensive on-site investigation took place. The scholars then returned to the university, notebooks crammed with data, where the task of writing the report was left to the team leader. Shortly thereafter the physicist returned to the farm, saying to the farmer, “I have the solution, but it works only in the case of spherical cows in a vacuum”.

A spherical cow is a humorous metaphor for highly simplified scientific models of complex real life phenomena.[2][3] The implication is that theoretical physicists will often reduce a problem to the simplest form they can imagine in order to make calculations more feasible, even though such simplification may hinder the model’s application to reality.

“Reducing a problem to the simplest form… even though such simplification may hinder the model’s application to reality” is Rapley’s charge against economics, and the charge is wholly misguided. “All models are wrong, some models are useful” as George Box put it.

What would physicists do?

But perhaps Rapley still has a point: maybe Real Scientists also make false assumptions, but less-false assumptions than economists. Perhaps if physicists did economics they would offer models that, while not perfectly realistic, are much more realistic than those offered by economists? Luckily, we don’t have to guess because for the last 20 years or so physicists have been doing economics, a branch of the literature now called “econophysics.”

The models used by econophysicists are much more abstract than even canonical neoclassical models and in particular are much less realistic when it comes to depicting human behavior. The most common behavioral assumption in econophysics is “zero intelligence,” which means that humans exhibit no intentional behavior whatsoever. There is no attempt to add psychological realism to these models and no econophysicist has ever “required” the assumption of zero intelligence “to be tested empirically,” presumably to John Rapley’s great confusion, because it is obviously false and any such test would fail.

But the complete lack of psychological realism is hardly the end of false assumptions routinely used by physicists studying social phenomena. Let’s consider an example paper and use a common rhetorical device of pop economic critics: we’ll write out their assumptions in English and mock them!

Jerico et al (2015) ask “When does inequality freeze an economy?” in the Journal of Statistical Mechanics: Theory and experiment. The authors wish to understand how changes in the degree of wealth inequality affect trades in an economy. Here are (some of) the assumptions of the model used:

The world consists of an arbitrary number of people who trade an arbitrary number of goods.

The number of people is fixed and people live forever.

The amounts of all goods are fixed and all goods last forever. There is no production.

All prices are given from outside the model and never change for any reason.

People behave identically and differ only in that they have different wealth and different sets of goods at the beginning of the world. These are assigned randomly at the beginning of time.

Everything that happens in this world is as follows: at each time, a good is randomly selected by nature. The owner of that good is obliged to offer it for sale to a randomly selected person for a price fixed before time began. If the randomly selected person offered the good has enough money to buy it, then the potential buyer must buy the good. This sequence of randomly selecting goods and buyers and forcing trades whenever budgets allow continues forever.

We seek the “thermodynamic equilibrium” in this world as time tends to infinity.

Obviously, all of these assumptions are really, really false and we don’t need to go test them empirically. And of course the authors know this, writing

The model presented in this paper is intentionally simple, so as to highlight a simple, robust and quantifiable link between inequality and liquidity. In particular, the model neglects important aspects such as (i) agents’ incentives and preferential trading, (ii) endogenous price dynamics and (iii) credit. It is worth discussing each of these issues in order to address whether the inclusion of some of these factors would revert our finding that inequality and liquidity are negatively related.

Pop critics such as John Rapley should at least understand why economists use models with obviously false assumptions, they should understand why such models can be useful even they are wrong, and they should be aware that all sciences, not just economics, routinely use models with false assumptions. Otherwise, they’ll continue to hurl models to the floor, angrily insisting they must be models for ants, while the audience laughs.

Over the past couple of weeks two studies of the effects of Seattle’s recent minimum wage hikes have been released: one from some researchers at the University of Washington, the other from researchers at Berkeley. The results have widely been interpreted, including by the authors of one of the studies, as wildly inconsistent. This post presents a non-technical guide to the problems associated with attempting to estimate the effects of the minimum wage, the statistical methods used in both papers, and how to interpret the results (spoiler: the results are actually not in conflict).

This post presents a simple explanation of the concept of “local average treatment effects” in the context of instrumental variables estimation. I borrow shamelessly from the somewhat more advanced presentation in Imbens and Wooldridge’s lecture notes, which is a good place to look for further reading.

The basic idea underlying LATE is to acknowledge that different people (or different units more generally) generally have different causal effects for any given “treatment,” broadly defined. It is common to talk about “the” causal effect of, say, education on earnings, or interest rates on growth, or pharmaceuticals on health, but if different people respond differently to education or to medical treatments and different countries respond differently to macroeconomic interventions, it’s not clear what we mean by “the” causal effect. We can still talk coherently about distributions of causal effects, though, and we may be interested in estimated various averages of those causal effects. Local average treatment effects (LATEs) are one such average.

For concreteness, let’s suppose the government decides to lend a hand to empirical researchers by implementing the following goofy policy: a randomly selected group of high school kids are randomized to get an offer of either $0 or $5,000 to acquire a college degree. We wish to use this natural experiment to estimate “the” effect of getting a college degree on, say, wages. We collect data on all these folks comprised of: a dummy variable which equals one if person was offered $5,000 and zero if they were offered zero, a dummy variable which indicates the student actually received a college degree, and wages, .

I make three points. First, that the article is much more consistent with the literature if one reads “poverty” every time the article uses the word “inequality.” Second, that the fact that income and health are correlated across people or across regions does not tell us that income causes health. And finally, that the research does suggest that decreasing poverty will increase health, but that we should not expect substantial reductions in health care expenditures as a result. I close with some notes on policy implications.

Much has been made of a recent Gallup poll showing a majority of Americans now support marijuana legalization. But if a majority support legalization, why do politicians seem so reluctant to support drug law reform?

One explanation for this puzzle is that Americans who vote are less likely to support legalization than those who do not vote. Voters tend to be older, and possibly have other characteristics which are associated with opposition to drug policy reform.